Data concierge-The Foundation of a Digital Business

1. Data Concierge –
The Foundation of a Digital Business
Thomas Dornis
San Francisco, CA – December 7th 2017
#CWIN17

2. CWIN San Francisco 2017 – Data Concierge| December 7th 2017
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Table of Contents
 Introduction/ Insights Drive Organization
 Data Concierge Approach
 Balancing Business Value with Industrialized
Capabilities

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Becoming an Insight Driven Organization – Intelligent Enterprise
A fact-driven, highly industrialized quick-scan of your insights & data portfolio, giving you all you need to
make informed, value-driven decisions about the next steps of your insight-driven journey.
 A high-speed, metrics-driven 6-12 weeks intervention that delivers
a business case, to-be design and roadmap for key portfolio
areas:
 BI Modernization
 Business Insights Service Center/ Data Concierge
 Big Data/ Data Science
 Cognitive & AI
 Sector and Domain Analytics
What We Do
The Value
 Client sees many opportunities to leverage insights & data, for
example around Big Data, Modernizing the BI Landscape, creating
advanced analytics and exploring Cognitive & AI
 Client has trouble in making decisions, being hesitant about the
financial impact, the technology choices, the to-be design and
most feasible next steps to make
Challenges & Opportunities
 Multiple years of tried & tested approach, delivering solid results in
a short timeframe, facilitating decision-making
 Dedicated and specialized Center of Excellence in Bangalore
 Collaborative process, involving all key stakeholders from day 1
 Tools-supported, compelling visual report outs

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Key transformation challenges for large scale data & analytics platforms
 End user profiles/ requirements vary widely across the organization
 Demand for new data assets become intolerable in a classic governance set up
 User stories backlogs are getting longer and longer even when employing Agile methods
 Deploying new services is taking too long
 New tools & analytical techniques proliferate
DATA CONCIERGE
Industrialize and automatize data provisioning processes as much as possible
Provide a simple, business-oriented information catalog of all data assets available
Provide a simple and managed way for business users to go “self service” where possible
Use intelligent processes for proactive optimization & recommendations

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Compressing the time to value, standardizing the cost to insight
 Business Information Catalog Services
 Repository, search and recommendation services for business meta-
data
 Data Operations Services
 On-going management and support of the data assets including
optimization, quality and governance
 Ingestion Services
 Loading data in appropriate perimeter with corresponding SLA and
on-demand / self-service features for the business
 Distillation Services
 Structuring and providing the business with the information they need
in the right view
 Data Science and Analytics Services
 A bespoke service for data science & analytics with multiple insights
delivery models
 Use Case Catalog Services
 Repository of solutions/ use cases that have been implemented with
business value and impact to bottlers
 “Art of the Possible”
Industrialized
Automatized
Intelligent
Data Concierge Framework

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Data Operations
Services
Data Concierge: Business Information Catalog Services
 Business description of datasets, structures & services
 Through a web-based portal, users can search for data assets within the lake, and use
recommendations provided by the tool; shopping cart approach for data assets
 Communicate data assets characteristics
 Ownership – IT & Business champions in charge of the data assets & contact info
 Perimeter & SLAs – industrial/certified, experiment, self-service (shareable)
 Access – which user population have access to this data asset, type of access
 Current status of accessibility/usability within the lake
 The data lake governance instances periodically review and curate the additions & modifications
made to the catalog
 Curation of data assets
 Governance of self service & experiment perimeters – discard data assets when initiatives are finalized
 Major communication tool to support user adoption
Ingestion
Services
Distillation
Services
Data Science
& Analytics
Services
Business
Information
Catalog Services
Use Case
Catalog Services

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Use Case
Catalog Services
Data Concierge: Data Operations (Governance) Services s
Intelligence behind the Data Concierge, masking complexity to the user
• Logs data assets search requests by user population
• Builds recommendations for data assets searched and used by similar populations of users
• Manage access rules between stakeholders (sharing of information, use etc.)
• Logs access to datasets, structures & services, distillation & transformation processes
• Detects anomalous search & access behaviors
• Apply quality & governance rules for data assets, structures & services
• Publish data assets and structures to perimeter, deploys services
• Enable & publish data lineage for each service
• Propose schema/structure optimization within a Data Hub/ Business Data Lake structure
• Cluster performance monitoring and optimization recommendations
Ingestion
Services
Distillation
Services
Data Science
& Analytics
Services
Business
Information
Catalog Services
Data Operations/
Services

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Use Case
Catalog Services
Business
Information
Catalog Services
Data Operations
Services
Data Concierge: Ingestion Services
 “Industrialized” Ingestion Services
 Support multiple modes of ingestion:
• Real-time streaming
• Micro-batch
• Batch
• Replication
 Govern choice of software vendors: Data Integration Platform, API
Services, Open Source tools
 Service Ticket driven approach
 Users request a system and data asset that they didn’t find in the catalog
 They receive a defined response time for loading
 Stage Key Data Sources
 80/20 Rule: 80% of analytics requirements are driven by 20% of data
 Stage common/ most used data sources to “seed” the data lake
Ingestion
Services
Distillation
Services
Data Science
& Analytics
Services
IngestionServices
Events
Web API
File
App API
Adaptor
RDBMS
StreamingBatchSOA/EAI
CDC
TechnicalMetaData
Audit
DataLake

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Use Case
Catalog Services
Ingestion
Services
Business
Information
Catalog Services
Data Operations
Services
Data Concierge: Distillation Services
 Conversion of RAW data into usable data
 Users request data assets to be converted into SQL data
stores with the right view for their needs
 Users can also request Sandboxes for investigation and
analytics
 Users can also request Excel and interactive reports
dashboards
 Users receive a defined response time for deployment
 Incorporate MDM and X-Ref data to create single
views of given domains (re-usable components)
 Aggregate massive data sets down to manageable
results volumes
 Includes the provisioning, and de-provisioning of
distillations on an automatic or scheduled basis
Distillation
Services
Data Science
& Analytics
Services
Master Data &
X-ref
Transformation
Aggregation
Data Lake
Distillation Layer
Usage Layer
Extraction
SQL SandboxSQL Excel
Provisioning

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Ingestion
Services
Distillation
Services
Business
Information
Catalog Services
Data Operations
Services
Data Concierge: Data Science & Analytics Services
 Question-based Data Science
 “Can we improve the forecast for next years sales? (12+ month view not in SAP/ APO?)”
 “What are the primary drivers for missed deliveries?”
 “Can we predict factory downtime?”
 Multiple delivery modes available depending on complexity and business users’ autonomy
 Users can request to be fully autonomous, or work in integrated team, or request a fully delivered service
 For an integrated team or fully delivered service, a CoE Data Science team can work collaboratively to define the problem
space, data requirements and definition of the outcome required
 A fixed price is then provided for the ‘proof of value’
 Once the model has been proven it can then be industrialized via the Ingestion and Distillation Services
 The different delivery modes can be used as a framework to progressively ramp up the end users on the
new system
 Enabling new data usages and new tools:
• Initial use cases are delivered in integrated mode; formal delivery by CoE or similar structure
• Over time, stakeholder may deliver their own use cases
 Use of data lab approach and exploration to help users get familiar with the data assets available and the functions of the new
system vs. legacy
Data Science
& Analytics
Services
Use Case
Catalog Services

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Data Concierge – Use Case Catalog
Ingestion
Services
Distillation
Services
Business
Information
Catalog Services
Data Operations
Services
Data Science
& Analytics
Services
Use Case
Catalog Services
Existing Use Cases New Use Cases+
Improve
Insights Catalog
Industrialize
PoC
Filter and Eliminate
Scale & Communicate

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Data Concierge – Use Case Catalog
Advanced Analytics/ Data Science Example (CPG)
Connected Assets & Service
Recommendation
• Analyze customer usage
pattern
• Recommend optimal services
offer
• Real-time Asset control
Product Design Analytics
• System Reliability Modeling
• Intelligent target setting &
allocation
• Cost & Weight Analytics
• Approximate models using
CAE/Test Data
• Product Benchmarking
EngineeringDirector
Supplier Risk Analytics
• Supplier quality analysis & Risk
Driver Identification
• Quality & Risk Scoring
• Rationalization & optimal
selection
EngineeringDirector
Factory Analytics
• Machine Performance
& Control
• Energy Consumption Analysis
• Predictive Machine
Maintenance
• Stochastic demand & supply
planning
Head-Manufacturing
Asset Performance & Control
• Performance Analysis
• Segmentation
• Adaptive Control Limits
• Real-time monitoring
Head-Operations
Predictive Maintenance
• Correlation of events/usage
to failures
• Root cause analysis & driver
identification
• Failure Prediction &
Recommendation Explore
failure anomalies
Head-Operations
CMO
Advanced Planning &
Scheduling
• Analyze disruptions &
operational impact
• Stochastic demand & supply
planning of resources (e.g.
assets, manpower, services,
etc.)
Head-Operations
Service/ Issue Analytics
• Financial budgeting & reserving
• Claims & supply side
optimization
• Recall & product improvement
modeling
• Coverage & pricing strategy
formulation
CMO&CFO
Service Optimization
• Dealer/service provider
performance analysis
• Predict the profitable customers
who may sign/renew services
contracts
• Service price optimization
CMO
• Data Science
“Applications” to
address specific use
cases
• Contributions by entire
system:
• Business Units
• Corporate
• CoE
• Vendors/ Integrators
• Shared Service manages
the industrialization/
scaling
• Apply when ready –
driven by stakeholder
maturity
Development Approach:

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Balancing Business Value & Industrialized Capabilities
End State
Quick Wins
Ideal
Architecture
Corridor
of balance
BusinessServiceValue
Capability
Value
Score-
carding Catalog
of Services
Training
Organisation
and Roles
routerequest
Customer
Business
Partner
Engagement
Support
Engagement
Core
Advanced
Custom
Interpretation
Internal
delivery
Walk up or Ad-
Hoc
Engagement
Walk up
Template Capability Centre
Person/screen
Capability Centre or
Cluster
Capability Centre or
Cluster
Brief
Demand Management Supply Management
Brief
EngagementEngagement
Projects EngagementProject Plan
Data &
Technology
Allocation
Resource
Allocation
Governance
Delivery
Prioritisation
Enterintodemandbacklog
Service
Selection
Brief Type Delivered By
Business Question
Defined
Delivery Plan
Delivery Processes
SUBSCRIPTION
PAY-AS-YOU-GO
PAY FOR FLEX
RESOURCES
Funding

14. CWIN San Francisco 2017 – Data Concierge| December 7th 2017
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Thank You!
Phone: +1 (520) 661-7333
thomas.dornis@capgemini.com
Thomas Dornis
NA Leader – Information Strategy
Insights & Data
Speaker 1
Photo

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Appendix

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Data Concierge: Services Mapped to the Data Hub/ Business Data Lake
Architecture
Data Lake
Distillation Layer
Usage Layer
ODS
Applications
Analytics & Data Science
Industrial, certified
Perimeter
Experiment
Perimeter
Self service
Perimeter
Business Information Catalog
Operations
MDM Transformation Aggregation Transformation
Aggregation
Transformation
Aggregation
Governance
Governance
Corporate
view
Local
view
..Sandbox
spaceN
Sandbox
space1
..Sandbox
spaceN
Sandbox
space1
Sources
Ingestion Services
Distillation Services
Data Science
& Analytics Services
Business Information
Catalog Services
Data Operations
Services
Data domains
Data domains
Data domains
Data domains
Data domains
Data domains
Data domains
Use Case
Catalog Services